1. Field
The present disclosure relates to a gaming system that distributes tasks for performance by human intelligence and collects task results.
2. Description of Related Art
Various processing tasks exist that are difficult to perform using an automated algorithm, but that are relatively trivial for a human operator. For example, there is a substantial need for identification, characterization, or classification of features of photographs, sounds and other digital data used to produce visual or auditory image output. This identification, characterization or classification either eludes computerized systems or requires human confirmation of computerized analysis. At least one computerized system exists to distribute such tasks to human operators in exchange for renumeration of some kind. For example, Amazon developed a system coined Mechanical Turk (http://www.mturk.comImturklwelcome) that pays humans to perform Human Intelligence Tasks. Mechanical Turk defines Human Intelligence Tasks as “simple tasks that people do better than computers.” As an example, a person may be able to perform the task of identifying whether a specific type of object (for example, a pizza parlor) appears in a photograph or video sequence easier and more efficiently than a computer.
This model, paying for people to perform tasks, fails where the cost of the labor to perform a task exceeds the value of the tasks. For example, a task may be to identify parking meters in a system similar to Google Earth™. The value to the company seeking the information may be only a tenth of a penny per parking meter. A human operator may perform the task a maximum of 250 times in an hour, on average. Performed by a person, this task may not make financial sense, even when pricing labor in cheap offshore outsourced labor markets.
Complicating this problem, humans often make errors even in those tasks that they are uniquely best suited to perform. Many errors arise through carelessness or just normal momentary lapses in concentration. Some people may intentionally enter incorrect data, either maliciously, or in order to boost their pay rate by creating false results. Accordingly, there is a need for a system that effectively distributes tasks and collects results for human intelligence tasks, for example visual identification tasks, in a more cost-effective manner and in a manner that prevents or corrects erroneous entries.
Various distributed computing systems are known, in which a server distributes processing jobs to a plurality of clients for performance by the client during processor idle time, and collects results. However, the processing tasks in those prior art systems are performed solely by the client processor in cooperation with a server, and do not involve or require human intelligence input. In addition, various methods and systems are known for distributing updated digital image or audio data to be output by a game engine during game play at one or more local clients, for advertising or game enhancement purposes. Some such prior systems also collect use information regarding user interaction with the updated data, e.g., number of views or clicks, and report the use information to a central server for analysis or control of distributed updates. However, such systems do not attempt to solve any defined problem through human interaction via game play. Problem solution through game play requires unique methods and solutions that have not been contemplated in any prior art system. Indeed, it has not been contemplated that game play can be used to solve problems requiring human intelligence input, especially problems involving the identification, characterization or classification of visual images or audible output based on qualities that humans are uniquely to recognize, but that are difficult or impossible to recognize using an automated algorithm.